This is the code and bibtex files for the paper Research trends in Coquerel’s sifaka (Propithecus coquereli)

#Descriptive analysis
results <- biblioAnalysis(M, sep = ";")
S <- summary(object = results, k = 10, pause = FALSE)
## 
## 
## MAIN INFORMATION ABOUT DATA
## 
##  Timespan                              1978 : 2022 
##  Sources (Journals, Books, etc)        35 
##  Documents                             92 
##  Average years from publication        10.1 
##  Average citations per documents       13.79 
##  Average citations per year per doc    1.229 
##  References                            3440 
##  
## DOCUMENT TYPES                     
##  article                         70 
##  article; proceedings paper      1 
##  editorial material              1 
##  meeting abstract                13 
##  note                            2 
##  proceedings paper               1 
##  review                          4 
##  
## DOCUMENT CONTENTS
##  Keywords Plus (ID)                    379 
##  Author's Keywords (DE)                269 
##  
## AUTHORS
##  Authors                               281 
##  Author Appearances                    350 
##  Authors of single-authored documents  8 
##  Authors of multi-authored documents   273 
##  
## AUTHORS COLLABORATION
##  Single-authored documents             8 
##  Documents per Author                  0.327 
##  Authors per Document                  3.05 
##  Co-Authors per Documents              3.8 
##  Collaboration Index                   3.25 
##  
## 
## Annual Scientific Production
## 
##  Year    Articles
##     1978        1
##     1983        1
##     1991        1
##     1992        2
##     1993        1
##     1995        1
##     1999        1
##     2000        1
##     2001        2
##     2002        1
##     2003        1
##     2004        3
##     2005        2
##     2006        2
##     2007        3
##     2008        1
##     2009        3
##     2010        2
##     2011        3
##     2012        6
##     2013        1
##     2014        9
##     2015        3
##     2016        9
##     2017        3
##     2018        8
##     2019        3
##     2020        8
##     2021        8
##     2022        2
## 
## Annual Percentage Growth Rate 1.587808 
## 
## 
## Most Productive Authors
## 
##    Authors        Articles Authors        Articles Fractionalized
## 1     GREENE LK          6    LEHMAN SM                      3.33
## 2     LEHMAN SM          6    ROSS AC                        2.00
## 3     GANZHORN JU        5    GANZHORN JU                    1.62
## 4     YODER AD           5    GREENE LK                      1.36
## 5     DREA CM            4    GARDNER CJ                     1.33
## 6     GUEVARA EE         4    MCGOOGAN KC                    1.33
## 7     CAMPBELL JL        3    DREA CM                        1.31
## 8     DONATI G           3    GRIESER B                      1.00
## 9     EISEMANN JH        3    MEADOR LM                      1.00
## 10    EPPLEY TM          3    NUNN CL                        1.00
## 
## 
## Top manuscripts per citations
## 
##                           Paper                                                               DOI TC TCperYear  NTC
## 1  SCORDATO ES, 2007, CHEM SENSES        10.1093/chemse/bjm018                                    83      5.19 2.62
## 2  RAVOSA MJ, 1993, AM J PHYS ANTHROPOL  10.1002/ajpa.1330920408                                  75      2.50 1.00
## 3  CAMPBELL JL, 2000, AM J PRIMATOL      10.1002/1098-2345(200011)52:3<133::AID-AJP2>3.0.CO;2-\\# 66      2.87 1.00
## 4  JACOBS GH, 2002, VISION RES           10.1016/S0042-6989(01)00264-4                            55      2.62 1.00
## 5  GODFREY LR, 2012, AM J PHYS ANTHROPOL 10.1002/ajpa.21615                                       55      5.00 2.80
## 6  DA SILVA AJ, 2003, VET PARASITOL      10.1016/S0304-4017(02)00384-9                            49      2.45 1.00
## 7  HAYES RA, 2004, AM J PRIMATOL         10.1002/ajp.20038                                        41      2.16 1.11
## 8  MAYOR MI, 2004, INT J PRIMATOL        10.1023/B:IJOP.0000029127.31190.e9                       39      2.05 1.05
## 9  MCKENNEY EA, 2015, PLOS ONE           10.1371/journal.pone.0124618                             39      4.88 2.49
## 10 PASTORINI J, 2001, AM J PRIMATOL      10.1002/1098-2345(200101)53:1<1::AID-AJP1>3.0.CO;2-J     38      1.73 1.49
## 
## 
## Corresponding Author's Countries
## 
##          Country Articles   Freq SCP MCP MCP_Ratio
## 1 USA                  51 0.6456  38  13     0.255
## 2 CANADA                5 0.0633   3   2     0.400
## 3 GERMANY               5 0.0633   1   4     0.800
## 4 UNITED KINGDOM        5 0.0633   3   2     0.400
## 5 FRANCE                4 0.0506   0   4     1.000
## 6 AUSTRALIA             3 0.0380   1   2     0.667
## 7 CHINA                 2 0.0253   2   0     0.000
## 8 JAPAN                 2 0.0253   0   2     1.000
## 9 PORTUGAL              2 0.0253   0   2     1.000
## 
## 
## SCP: Single Country Publications
## 
## MCP: Multiple Country Publications
## 
## 
## Total Citations per Country
## 
##     Country      Total Citations Average Article Citations
## 1 USA                        938                      18.4
## 2 GERMANY                     62                      12.4
## 3 AUSTRALIA                   53                      17.7
## 4 CANADA                      51                      10.2
## 5 FRANCE                      51                      12.8
## 6 JAPAN                       43                      21.5
## 7 UNITED KINGDOM              39                       7.8
## 8 PORTUGAL                    32                      16.0
## 9 CHINA                        0                       0.0
## 
## 
## Most Relevant Sources
## 
##                                                                Sources        Articles
## 1  AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY                                        15
## 2  AMERICAN JOURNAL OF PRIMATOLOGY                                                  13
## 3  INTERNATIONAL JOURNAL OF PRIMATOLOGY                                             12
## 4  PLOS ONE                                                                          5
## 5  PRIMATES                                                                          5
## 6  JOURNAL OF ZOO AND WILDLIFE MEDICINE                                              4
## 7  ZOO BIOLOGY                                                                       4
## 8  FOLIA PRIMATOLOGICA                                                               3
## 9  ANATOMICAL RECORD-ADVANCES IN INTEGRATIVE ANATOMY AND EVOLUTIONARY BIOLOGY        2
## 10 ANIMAL BEHAVIOUR                                                                  2
## 
## 
## Most Relevant Keywords
## 
##    Author Keywords (DE)      Articles          Keywords-Plus (ID)     Articles
## 1      PROPITHECUS                 14 MADAGASCAR                            14
## 2      MADAGASCAR                  10 PROPITHECUS-VERREAUXI-COQUERELI       12
## 3      LEMUR                        9 HAPALEMUR-GRISEUS                     11
## 4      COQUEREL'S SIFAKA            7 VARECIA-VARIEGATA                     11
## 5      LEMURS                       7 BEHAVIOR                              10
## 6      PRIMATES                     7 EVOLUTION                             10
## 7      PROPITHECUS COQUERELI        7 PRIMATES                              10
## 8      PROSIMIAN                    5 FOREST                                 7
## 9      SIFAKA                       4 PRIMATE                                7
## 10     STREPSIRRHINES               4 RING-TAILED LEMURS                     6
knitr::kable(S$MainInformationDF, caption = "Summary Information") #main information
Summary Information
Description Results
MAIN INFORMATION ABOUT DATA
Timespan 1978:2022
Sources (Journals, Books, etc) 35
Documents 92
Average years from publication 10.1
Average citations per documents 13.79
Average citations per year per doc 1.229
References 3440
DOCUMENT TYPES
article 70
article; proceedings paper 1
editorial material 1
meeting abstract 13
note 2
proceedings paper 1
review 4
DOCUMENT CONTENTS
Keywords Plus (ID) 379
Author’s Keywords (DE) 269
AUTHORS
Authors 281
Author Appearances 350
Authors of single-authored documents 8
Authors of multi-authored documents 273
AUTHORS COLLABORATION
Single-authored documents 8
Documents per Author 0.327
Authors per Document 3.05
Co-Authors per Documents 3.8
Collaboration Index 3.25
knitr::kable(S$MostProdAuthors, caption = "Most Productive Authors") #Most productive Authors
Most Productive Authors
Authors Articles Authors Articles Fractionalized
GREENE LK 6 LEHMAN SM 3.33
LEHMAN SM 6 ROSS AC 2.00
GANZHORN JU 5 GANZHORN JU 1.62
YODER AD 5 GREENE LK 1.36
DREA CM 4 GARDNER CJ 1.33
GUEVARA EE 4 MCGOOGAN KC 1.33
CAMPBELL JL 3 DREA CM 1.31
DONATI G 3 GRIESER B 1.00
EISEMANN JH 3 MEADOR LM 1.00
EPPLEY TM 3 NUNN CL 1.00
knitr::kable(S$MostCitedPapers, caption = "Most Cited Papers") #most cited paper
Most Cited Papers
Paper DOI TC TCperYear NTC
SCORDATO ES, 2007, CHEM SENSES 10.1093/chemse/bjm018 83 5.19 2.62
RAVOSA MJ, 1993, AM J PHYS ANTHROPOL 10.1002/ajpa.1330920408 75 2.50 1.00
CAMPBELL JL, 2000, AM J PRIMATOL 10.1002/1098-2345(200011)52:3<133::AID-AJP2>3.0.CO;2-# 66 2.87 1.00
JACOBS GH, 2002, VISION RES 10.1016/S0042-6989(01)00264-4 55 2.62 1.00
GODFREY LR, 2012, AM J PHYS ANTHROPOL 10.1002/ajpa.21615 55 5.00 2.80
DA SILVA AJ, 2003, VET PARASITOL 10.1016/S0304-4017(02)00384-9 49 2.45 1.00
HAYES RA, 2004, AM J PRIMATOL 10.1002/ajp.20038 41 2.16 1.11
MAYOR MI, 2004, INT J PRIMATOL 10.1023/B:IJOP.0000029127.31190.e9 39 2.05 1.05
MCKENNEY EA, 2015, PLOS ONE 10.1371/journal.pone.0124618 39 4.88 2.49
PASTORINI J, 2001, AM J PRIMATOL 10.1002/1098-2345(200101)53:1<1::AID-AJP1>3.0.CO;2-J 38 1.73 1.49
plot(x = results, k = 10, pause = FALSE)

## Warning: Removed 2 rows containing missing values (position_stack).

## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 row(s) containing missing values (geom_path).

#Top-Authors’ Productivity over the Time:
topAU <- authorProdOverTime(M, k = 10, graph = TRUE)

#Chart of Paper per decade
G <- M %>%
  mutate(Decade = as.numeric(PY) - as.numeric(PY) %% 10) %>%
  group_by(Decade) %>%
  summarize(val = n()) %>%
  ungroup()

G$Decade = as.factor(G$Decade)

ggplot2::ggplot(G, aes(x = Decade, y = val)) +
  geom_bar(stat = "identity", aes(fill = factor(Decade))) +
  scale_x_discrete(labels = G %>% distinct(Decade) %>% mutate(Decade = paste0(Decade, "s")) %>% pull()) +
  geom_text(aes(label = format(val, big.mark = ",")), size = 5, vjust =
              -0.3) +
  ggtitle('Number of Papers per Decade') +
  theme(
    panel.border = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    legend.position = "none",
    plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
    text = element_text(),
    panel.background = element_rect(fill = "white"),
    plot.background = element_rect(colour = NA),
    axis.title = element_text(face = "bold", size = rel(1)),
    axis.text = element_text(size = 16),
    axis.line = element_line(colour = "black"),
    panel.grid.major = element_line(colour = "#f0f0f0"),
    panel.grid.minor = element_blank(),
    axis.ticks = element_line(colour = "black"),
    plot.margin = unit(c(10, 5, 5, 5), "mm"),
    strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
    strip.text = element_text(face = "bold")
  )

#Co-word analysis: cluster terms extracted from keywords, titles, or abstracts
NetMatrix <-
  biblioNetwork(M,
                analysis = "co-occurrences",
                network = "keywords",
                sep = ";")
netplot = networkPlot(
  NetMatrix,
  normalize = "association",
  weighted = T,
  n = 50,
  Title = "Keyword Co-occurrences",
  type = "auto",
  cluster = "louvain",
  community.repulsion = 0.15,
  size = T,
  edgesize = 7,
  labelsize = 1,
  remove.multiple = TRUE,
  remove.isolates = T
)

#perform multiple correspondence analysis (MCA): identify clusters of documents that express common concepts
CS <-
  conceptualStructure(
    M,
    field = "ID",
    method = "MCA",
    minDegree = 5,
    clust = 4 ,
    k.max = 5,
    stemming = FALSE,
    labelsize = 15
  )
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.

#trend topics by year
res <-
  fieldByYear(
    M,
    field = "ID",
    timespan = c(1978, 2022),
    min.freq = 5,
    n.items = 5,
    graph = TRUE
  )

#Thematic Map -  starts from a co-occurrence keyword network to plot in a two-dimensional map the themes of a domain.
Map = thematicMap(
  M,
  field = "ID",
  n = 55,
  minfreq = 4,
  stemming = TRUE,
  size = 0.7,
  n.labels = 3
)
plot(Map$map)

Clusters = Map$words[order(Map$words$Cluster, -Map$words$Occurrences), ]
CL <-
  Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 27 x 5
## # Groups:   Cluster_Label [4]
##    Occurrences Words                           Cluster Color     Cluster_Label                  
##          <dbl> <chr>                             <dbl> <chr>     <chr>                          
##  1          14 madagascar                            1 #E41A1C80 madagascar                     
##  2          10 behavior                              1 #E41A1C80 madagascar                     
##  3          10 evolution                             1 #E41A1C80 madagascar                     
##  4          10 primates                              1 #E41A1C80 madagascar                     
##  5           7 forest                                1 #E41A1C80 madagascar                     
##  6          12 propithecus-verreauxi-coquereli       2 #377EB880 propithecus-verreauxi-coquereli
##  7          11 hapalemur-griseus                     2 #377EB880 propithecus-verreauxi-coquereli
##  8          11 varecia-variegata                     2 #377EB880 propithecus-verreauxi-coquereli
##  9           6 ring-tailed lemurs                    2 #377EB880 propithecus-verreauxi-coquereli
## 10           5 gastrointestinal-tract                2 #377EB880 propithecus-verreauxi-coquereli
## # ... with 17 more rows
# Keyword growth
topkw = KeywordGrowth(
  M,
  Tag = "ID",
  sep = ";",
  top = 15,
  cdf = TRUE
)
## Joining, by = "Tab"
topkw$PRIMATES <- topkw$PRIMATES + topkw$PRIMATE
topkw$LEMURS <- topkw$LEMURS + topkw$LEMUR
topkw$`PROPITHECUS-VERREAUXI-COQUERELI` <-
  topkw$`PROPITHECUS-VERREAUXI-COQUERELI` + topkw$VERREAUXI
topkw <- select(topkw,-PRIMATE)
topkw <- select(topkw,-LEMUR)
topkw <- select(topkw,-`RING-TAILED LEMURS`)
topkw <- select(topkw,-VERREAUXI)
topkw <- select(topkw,-`GASTROINTESTINAL-TRACT`)
topkw = rename(topkw, P.V.COQUERELI = `PROPITHECUS-VERREAUXI-COQUERELI`)
topkw <- subset(topkw, Year >= 1990)
DF = reshape::melt(topkw, id = 'Year') # reshape original data structure

alltopkw = KeywordGrowth(
  allM,
  Tag = "ID",
  sep = ";",
  top = 15,
  cdf = TRUE
)
## Joining, by = "Tab"
alltopkw$PRIMATES <- alltopkw$PRIMATES + alltopkw$PRIMATE
alltopkw <- select(alltopkw,-PRIMATE)
alltopkw = rename(alltopkw, P.D.EDWARDSI = `PROPITHECUS-DIADEMA-EDWARDSI`)
alltopkw <- subset(alltopkw, Year >= 1990)
allDF = reshape::melt(alltopkw, id = 'Year') # reshape original data structure

lemurtopkw = KeywordGrowth(
  lemurs,
  Tag = "ID",
  sep = ";",
  top = 15,
  cdf = TRUE
)
## Joining, by = "Tab"
lemurtopkw$PRIMATES <- lemurtopkw$PRIMATES + lemurtopkw$PRIMATE
lemurtopkw$LEMURS <- lemurtopkw$LEMURS + lemurtopkw$LEMUR
lemurtopkw <- select(lemurtopkw,-PRIMATE)
lemurtopkw <- select(lemurtopkw,-LEMUR)
lemurtopkw <- select(lemurtopkw,-POPULATION)
lemurtopkw <- select(lemurtopkw,-CONSERVATION)
lemurtopkw <- select(lemurtopkw,-`MICROCEBUS-MURINUS`)
lemurtopkw <- subset(lemurtopkw, Year >= 1990)
lemurDF = reshape::melt(lemurtopkw, id = 'Year') # reshape original data structure

update_geom_defaults("text", list(size = 2.8))

ggplot(NULL, aes(Year, value, group = variable)) +
  geom_line(data = lemurDF, aes(color = "black")) +
  geom_line(data = allDF, aes(color = "blue")) +
  geom_line(data = DF, aes(color = "red")) +
  scale_shape_manual(values = 1:15) +
  scale_x_continuous(breaks = seq(1990, max(DF$Year), by = 10)) +
  scale_y_continuous() +
  labs(
    y = "Count",
    variable = "Keywords",
    colour = "Search Term:",
    title = "Keywords Usage Evolution Over Time"
  ) +
  scale_color_manual(
    labels = c("Lemur*", "Propithecus", "P. Coquereli"),
    values = c("black", "blue", "red")
  ) +
  facet_wrap(variable ~ ., ncol = 4, scales = "free") +
  geom_text(
    data = DF %>%
      arrange(desc(Year)) %>%
      group_by(variable) %>%
      slice(1),
    aes(label = value),
    position = position_nudge(2),
    hjust = 0.5,
    show.legend = FALSE
  ) +
  geom_text(
    data = allDF %>%
      arrange(desc(Year)) %>%
      group_by(variable) %>%
      slice(1),
    aes(label = value),
    position = position_nudge(2),
    hjust = 0.5,
    show.legend = FALSE
  ) +
  geom_text(
    data = lemurDF %>%
      arrange(desc(Year)) %>%
      group_by(variable) %>%
      slice(1),
    aes(label = value),
    position = position_nudge(2),
    hjust = 0.5,
    show.legend = FALSE
  ) +
  
  theme(
    panel.border = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    plot.title = element_text(face = "bold", size = 12),
    text = element_text(size = 10),
    panel.background = element_rect(fill = "white"),
    plot.background = element_rect(colour = NA),
    axis.text = element_text(size = 10),
    axis.line = element_line(colour = "black"),
    panel.grid.major = element_line(colour = "#f0f0f0"),
    panel.grid.minor = element_blank(),
    axis.ticks = element_line(colour = "black"),
    plot.margin = unit(c(5, 5, 5, 5), "mm"),
    strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
    legend.position = "top",
    legend.justification = "right",
    legend.box.margin = margin(c(-27.5, 5, 5, 5)),
    legend.text =  element_text(size = 10),
    strip.text = element_text(face = "bold")
  )

# Three field plots
threeFieldsPlot(M, fields = c("JI", "AU", "ID"), n = c(10, 10, 25))
#The summary statistics of the network; The main indices of centrality and prestige of vertices.
NetMatrix <-
  biblioNetwork(M,
                analysis = "co-occurrences",
                network = "keywords",
                sep = ";")
netstat <- networkStat(NetMatrix)
summary(netstat, k = 10)
## 
## 
## Main statistics about the network
## 
##  Size                                  379 
##  Density                               0.034 
##  Transitivity                          0.399 
##  Diameter                              6 
##  Degree Centralization                 0.204 
##  Average path length                   2.742 
## 
#Thematic Evolution Analysis
nexus <-
  thematicEvolution(
    M,
    field = "ID",
    years = c(2000, 2010, 2020),
    n = 100,
    minFreq = 2
  )
plotThematicEvolution(nexus$Nodes, nexus$Edges)
# Create a historical citation network
histResults <- histNetwork(M, sep = ";")
## 
## WOS DB:
## Searching local citations (LCS) by reference items (SR) and DOIs...
## 
## Analyzing 4700 reference items...
## 
## Found 42 documents with no empty Local Citations (LCS)
net <- histPlot(histResults,
                n = 17,
                size = 7,
                labelsize = 5)

## 
##  Legend
## 
##                                                                    Label                                DOI Year LCS
## 1                  EAGLEN RH, 1978, FOLIA PRIMATOL DOI 10.1159/000155864                  10.1159/000155864 1978   4
## 2            KUBZDELA KS, 1992, AM J PRIMATOL DOI 10.1002/AJP.1350280206             10.1002/ajp.1350280206 1992   4
## 3       RAVOSA MJ, 1993, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.1330920408            10.1002/ajpa.1330920408 1993   4
## 4  MAYOR MI, 2004, INT J PRIMATOL DOI 10.1023/B:IJOP.0000029127.31190.E9 10.1023/B:IJOP.0000029127.31190.e9 2004   7
## 5                    HAYES RA, 2004, AM J PRIMATOL DOI 10.1002/AJP.20038                  10.1002/ajp.20038 2004   2
## 6                 CAMPBELL JL, 2004, AM J PRIMATOL DOI 10.1002/AJP.20081                  10.1002/ajp.20081 2004  12
## 7         BASTIAN ML, 2007, INT J PRIMATOL DOI 10.1007/S10764-007-9115-Y          10.1007/s10764-007-9115-y 2007   2
## 8               SCORDATO ES, 2007, CHEM SENSES DOI 10.1093/CHEMSE/BJM018              10.1093/chemse/bjm018 2007   3
## 9        YAMASHITA N, 2008, INT J PRIMATOL DOI 10.1007/S10764-008-9232-2          10.1007/s10764-008-9232-2 2008   5
## 10             SIMMEN B, 2010, PLOS ONE DOI 10.1371/JOURNAL.PONE.0009860       10.1371/journal.pone.0009860 2010   2
## 11         FICHTEL C, 2011, INT J PRIMATOL DOI 10.1007/S10764-010-9472-9          10.1007/s10764-010-9472-9 2011   2
## 12            KUN-RODRIGUES C, 2014, AM J PRIMATOL DOI 10.1002/AJP.22243                  10.1002/ajp.22243 2014   8
## 13            SALMONA J, 2014, ENDANGER SPECIES RES DOI 10.3354/ESR00622                   10.3354/esr00622 2014   5
## 14              REA MS, 2014, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.22409                 10.1002/ajpa.22409 2014   2
## 15              GARDNER CJ, 2015, PRIMATES DOI 10.1007/S10329-015-0462-6          10.1007/s10329-015-0462-6 2015   3
## 16       GRANATOSKY MC, 2016, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.22991                 10.1002/ajpa.22991 2016   3
## 17            SATO H, 2016, INT J PRIMATOL DOI 10.1007/S10764-015-9877-6          10.1007/s10764-015-9877-6 2016   4
## 18           EPPLEY TM, 2016, AM J PHYS ANTHROPOL DOI 10.1002/AJPA.23034                 10.1002/ajpa.23034 2016   3
## 19                     BRAY J, 2017, AM J PRIMATOL DOI 10.1002/AJP.22648                  10.1002/ajp.22648 2017   2
##    GCS
## 1   11
## 2   24
## 3   75
## 4   39
## 5   41
## 6   31
## 7    6
## 8   83
## 9   22
## 10  35
## 11  19
## 12  21
## 13  11
## 14   8
## 15   8
## 16  21
## 17  25
## 18  14
## 19  10
# biblioshiny()